| 引用本文: | 陈俊风,任子武,伞 冶.径向基函数神经网络的一种两级学习方法[J].控制理论与应用,2008,25(4):655~660.[点击复制] |
| CHEN Jun-feng,REN Zi-wu,SAN Ye.A two-level learning hierarchy for the radial basis function networks[J].Control Theory and Technology,2008,25(4):655~660.[点击复制] |
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| 径向基函数神经网络的一种两级学习方法 |
| A two-level learning hierarchy for the radial basis function networks |
| 摘要点击 1669 全文点击 1623 投稿时间：2006-08-09 修订日期：2007-12-25 |
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| DOI编号 10.7641/j.issn.1000-8152.2008.4.012 |
| 2008,25(4):655-660 |
| 中文关键词 径向基网络 两级学习 建模 泛化能力 |
| 英文关键词 radial basis function networks two-level learning hierarchy modeling generalization ability |
| 基金项目 国家自然科学基金资助项目(60474069). |
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| 中文摘要 |
| 建立RBF(radial basis function)神经网络模型关键在于确定网络隐中心向量、基宽度参数和隐节点数. 为设计结构简单, 且具有良好泛化性能径向基网络结构, 本文提出了一种RBF网络的两级学习新设计方法. 该方法在下级由正则化正交最小二乘法与D-最优试验设计结合算法自动构建结构节俭的RBF网络模型; 在上级通过粒子群优化算法优选结合算法中影响网络泛化性能的3个学习参数, 即基宽度参数、正则化系数和D-最优代价系数的最佳参数组合. 仿真实例表明了该方法的有效性. |
| 英文摘要 |
| The key to construct a radial basis function(RBF) network is to select reasonable hidden center vectors, RBF width and hidden node number. In order to design a RBF network with parsimonious structure and good generalization, a new two-level learning hierarchy for designing RBF networks is proposed. At the lower level in this method, a parsimonious RBF model is constructed by an integrated algorithm (ROLS+D-opt) which combines regularized orthogonal least squares (ROLS) with D-optimality experimental design (D-opt). At the upper level, particle swarm optimization (PSO) is used to search the optimal combination of three important learning parameters, i.e., the RBF width, the regularized parameter and D-optimality weight parameter, which influence the network’s generalization ability. Simulation results show the effectiveness of the proposed method. |
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